A pilot receives information from many sources during take-off, flight, and landing of an aircraft. The aircraft includes avionic systems designed to collect data, perform calculations on the data, and present the data to the pilot. For example, the aircraft may include an inertial navigation system (INS), an air data computer, a roll-pitch-yaw computer, a mission computer, various displays, and other avionic systems.
Some avionic systems may include one or more sensors that collect data, such as attitude, heading, altitude, and air speed. For example the INS may include three orthogonally mounted acceleration sensors and three nominally orthogonally mounted inertial angular rate sensors, which can provide three-axis acceleration and angular rate measurement signals. The same or other avionic systems may process this data. Avionic displays may present the data to the pilot in a usable format.
The redundancy of information may improve the accuracy of some avionic systems. For example, the aircraft may include both an INS and a global positioning satellite (GPS) receiver, or other radio frequency (RF) ranging system, such as Time Difference of Arrival (TDOA) and Galileo. Both the INS and the GPS receiver may provide estimates of the aircraft's position. The data from the GPS receiver may be used to calibrate the INS, while the GPS receiver may use the data from the INS to quickly re-establish tracking of a satellite in which the GPS receiver has temporarily lost contact. Thus, the integration of the INS and GPS receiver provides more accurate and robust data to the pilot.
Deep integration of the INS and the GPS receiver is a technique in which the tracking loops of the GPS receiver are closed or driven as part of the operation of an integrated navigation INS/GPS Kalman filter. Kalman filtering is a statistical technique that combines knowledge of the statistical nature of system errors with knowledge of system dynamics, as represented as a state space model, to arrive at an estimate of the state of a system. The INS/GPS Deep integration Kalman filter processes measurements from the INS and all available satellites using in-phase (I) and quadrature (Q) signals. The Kalman filter calculates the errors and sends correction data to a navigation processor, which provides as an output a navigation solution. By combining information from multiple satellites and the INS, the deeply integrated system is able to track the satellites under higher interference or jamming levels.
One challenge that arises in a deeply integrated INS/GPS system is handling problems that may occur when the GPS signal dynamics are not kinematically consistent with vehicle dynamics. This inconsistency may occur because the Kalman filter is trying to closely match the behavior of the GPS tracking loop with what it observes from the INS. For example, the GPS signal dynamics may not be kinematically consistent with vehicle dynamics due to GPS signal multipath. With multipath, a signal arrives at the GPS receiver via multiple paths due to reflections from the Earth and nearby objects, such as buildings and vehicles. Multipath can cause swings in GPS pseudorange with no corresponding change in vehicle dynamics. These kinematic inconsistencies may cause the Kalman filter to lose track, which may result in an erroneous navigation solution.
Thus, it would be beneficial to design the Kalman filter to account for multipath error so that the Kalman filter can continue providing accurate estimates to the navigation processor despite multipath conditions.